Bibliographic record
Abstract
The increasing use of AI technologies by judges for consideration and resolution of cases is inevitable. This transformation is evident in various judicial proceedings and is closely linked to the pressing issue of integrating artificial intelligence into the activities of courts. This article examines the training of judges in the safe use of AI tools. The term “safe use” reflects a cautiously positive approach to new technologies, achieved through raising awareness of artificial intelligence and mastering the skills required for providing automated task performance, while adhering to principles and following recommendations. The study considers AI tools for judiciary as modern technologies and systems that enable judges and other legal practitioners to achieve results that previously required human thought processes. The author gives examples of tasks and functions in the legal area that can be automated to meet specific needs. Additionally, he addresses common misconceptions about artificial intelligence and introduces principles from Draft UNESCO Guidelines for the Use of AI Systems in Courts and Tribunals aimed at minimizing associated risks. The study also highlights recent guidelines from the judiciary officials of Canada, New Zealand, and the United Kingdom, which contain useful suggestions aligned with ethical principles of AI and main ideas of justice. The article focuses on the importance of the prerequisites for the safe use in light of the emergence of GenAI chatbots. These chatbots operate based on user’s prompts and are extensively used across various areas and professions, including the area of law and judges. The study’s findings could be used as supplementary materials to develop recommendations for Ukrainian judges, with the intention of making further amendments to the Code of Judicial Ethics. Keywords: artificial intelligence, AI tools, artificial intelligence in judiciary, use of artificial intelligence by judges, Code of Judicial Ethics, AI hallucination, ChatGPT, AI systems
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.206 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".